Overview

Dataset statistics

Number of variables18
Number of observations12330
Missing cells0
Missing cells (%)0.0%
Duplicate rows125
Duplicate rows (%)1.0%
Total size in memory2.9 MiB
Average record size in memory247.1 B

Variable types

NUM14
BOOL2
CAT2

Warnings

Dataset has 125 (1.0%) duplicate rows Duplicates
ExitRates is highly correlated with BounceRatesHigh correlation
BounceRates is highly correlated with ExitRatesHigh correlation
Administrative has 5768 (46.8%) zeros Zeros
Administrative_Duration has 5903 (47.9%) zeros Zeros
Informational has 9699 (78.7%) zeros Zeros
Informational_Duration has 9925 (80.5%) zeros Zeros
ProductRelated_Duration has 755 (6.1%) zeros Zeros
BounceRates has 5518 (44.8%) zeros Zeros
PageValues has 9600 (77.9%) zeros Zeros
SpecialDay has 11079 (89.9%) zeros Zeros

Reproduction

Analysis started2020-09-05 02:11:45.186931
Analysis finished2020-09-05 02:12:12.882430
Duration27.7 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Administrative
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.315166261
Minimum0
Maximum27
Zeros5768
Zeros (%)46.8%
Memory size96.5 KiB
2020-09-04T21:12:12.949398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.321784106
Coefficient of variation (CV)1.434792897
Kurtosis4.701146249
Mean2.315166261
Median Absolute Deviation (MAD)1
Skewness1.960357209
Sum28546
Variance11.03424965
MonotocityNot monotonic
2020-09-04T21:12:13.081338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
0576846.8%
 
1135411.0%
 
211149.0%
 
39157.4%
 
47656.2%
 
55754.7%
 
64323.5%
 
73382.7%
 
82872.3%
 
92251.8%
 
101531.2%
 
111050.9%
 
12860.7%
 
13560.5%
 
14440.4%
 
15380.3%
 
16240.2%
 
17160.1%
 
18120.1%
 
196< 0.1%
 
244< 0.1%
 
224< 0.1%
 
233< 0.1%
 
202< 0.1%
 
212< 0.1%
 
Other values (2)2< 0.1%
 
ValueCountFrequency (%) 
0576846.8%
 
1135411.0%
 
211149.0%
 
39157.4%
 
47656.2%
 
55754.7%
 
64323.5%
 
73382.7%
 
82872.3%
 
92251.8%
 
ValueCountFrequency (%) 
271< 0.1%
 
261< 0.1%
 
244< 0.1%
 
233< 0.1%
 
224< 0.1%
 
212< 0.1%
 
202< 0.1%
 
196< 0.1%
 
18120.1%
 
17160.1%
 

Administrative_Duration
Real number (ℝ≥0)

ZEROS

Distinct3335
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.81861054
Minimum0
Maximum3398.75
Zeros5903
Zeros (%)47.9%
Memory size96.5 KiB
2020-09-04T21:12:13.215261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q393.25625
95-th percentile348.2663691
Maximum3398.75
Range3398.75
Interquartile range (IQR)93.25625

Descriptive statistics

Standard deviation176.7791075
Coefficient of variation (CV)2.187356431
Kurtosis50.55673905
Mean80.81861054
Median Absolute Deviation (MAD)7.5
Skewness5.615719019
Sum996493.468
Variance31250.85284
MonotocityNot monotonic
2020-09-04T21:12:13.357179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0590347.9%
 
4560.5%
 
5530.4%
 
7450.4%
 
11420.3%
 
6410.3%
 
14370.3%
 
9350.3%
 
15330.3%
 
10320.3%
 
19290.2%
 
13290.2%
 
12280.2%
 
21280.2%
 
8260.2%
 
3260.2%
 
18260.2%
 
20260.2%
 
17250.2%
 
37240.2%
 
23240.2%
 
33200.2%
 
26200.2%
 
34190.2%
 
47190.2%
 
Other values (3310)568446.1%
 
ValueCountFrequency (%) 
0590347.9%
 
1.3333333331< 0.1%
 
2150.1%
 
3260.2%
 
3.54< 0.1%
 
4560.5%
 
4.3333333331< 0.1%
 
4.52< 0.1%
 
4.751< 0.1%
 
5530.4%
 
ValueCountFrequency (%) 
3398.751< 0.1%
 
2720.51< 0.1%
 
2657.3180561< 0.1%
 
2629.2539681< 0.1%
 
2407.423811< 0.1%
 
2156.1666671< 0.1%
 
2137.1127451< 0.1%
 
2086.751< 0.1%
 
2047.2348481< 0.1%
 
1951.2791411< 0.1%
 

Informational
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.503568532
Minimum0
Maximum24
Zeros9699
Zeros (%)78.7%
Memory size96.5 KiB
2020-09-04T21:12:13.491124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.270156426
Coefficient of variation (CV)2.522310957
Kurtosis26.93226626
Mean0.503568532
Median Absolute Deviation (MAD)0
Skewness4.03646376
Sum6209
Variance1.613297346
MonotocityNot monotonic
2020-09-04T21:12:13.596063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
0969978.7%
 
110418.4%
 
27285.9%
 
33803.1%
 
42221.8%
 
5990.8%
 
6780.6%
 
7360.3%
 
9150.1%
 
8140.1%
 
1070.1%
 
125< 0.1%
 
142< 0.1%
 
111< 0.1%
 
131< 0.1%
 
241< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
0969978.7%
 
110418.4%
 
27285.9%
 
33803.1%
 
42221.8%
 
5990.8%
 
6780.6%
 
7360.3%
 
8140.1%
 
9150.1%
 
ValueCountFrequency (%) 
241< 0.1%
 
161< 0.1%
 
142< 0.1%
 
131< 0.1%
 
125< 0.1%
 
111< 0.1%
 
1070.1%
 
9150.1%
 
8140.1%
 
7360.3%
 

Informational_Duration
Real number (ℝ≥0)

ZEROS

Distinct1258
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.47239793
Minimum0
Maximum2549.375
Zeros9925
Zeros (%)80.5%
Memory size96.5 KiB
2020-09-04T21:12:13.718061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile195
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation140.7492944
Coefficient of variation (CV)4.082956304
Kurtosis76.31685309
Mean34.47239793
Median Absolute Deviation (MAD)0
Skewness7.579184716
Sum425044.6664
Variance19810.36388
MonotocityNot monotonic
2020-09-04T21:12:13.855155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0992580.5%
 
9330.3%
 
6260.2%
 
10260.2%
 
7260.2%
 
13230.2%
 
12230.2%
 
8220.2%
 
16220.2%
 
11210.2%
 
17180.1%
 
15180.1%
 
5180.1%
 
23170.1%
 
4170.1%
 
14170.1%
 
18170.1%
 
3160.1%
 
20140.1%
 
21140.1%
 
27120.1%
 
19120.1%
 
2110.1%
 
31110.1%
 
56100.1%
 
Other values (1233)196115.9%
 
ValueCountFrequency (%) 
0992580.5%
 
13< 0.1%
 
1.51< 0.1%
 
2110.1%
 
2.51< 0.1%
 
3160.1%
 
3.51< 0.1%
 
4170.1%
 
5180.1%
 
5.53< 0.1%
 
ValueCountFrequency (%) 
2549.3751< 0.1%
 
2256.9166671< 0.1%
 
2252.0333331< 0.1%
 
2195.31< 0.1%
 
2166.51< 0.1%
 
2050.4333331< 0.1%
 
1949.1666671< 0.1%
 
1830.51< 0.1%
 
1779.1666671< 0.1%
 
17781< 0.1%
 

ProductRelated
Real number (ℝ≥0)

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.73146796
Minimum0
Maximum705
Zeros38
Zeros (%)0.3%
Memory size96.5 KiB
2020-09-04T21:12:13.988159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median18
Q338
95-th percentile109
Maximum705
Range705
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.4755033
Coefficient of variation (CV)1.401621361
Kurtosis31.21170665
Mean31.73146796
Median Absolute Deviation (MAD)13
Skewness4.341516416
Sum391249
Variance1978.070394
MonotocityNot monotonic
2020-09-04T21:12:14.120796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16225.0%
 
24653.8%
 
34583.7%
 
44043.3%
 
63963.2%
 
73913.2%
 
53823.1%
 
83703.0%
 
103302.7%
 
93172.6%
 
123132.5%
 
113082.5%
 
132892.3%
 
152702.2%
 
162602.1%
 
142512.0%
 
172261.8%
 
202251.8%
 
192181.8%
 
222131.7%
 
182001.6%
 
211991.6%
 
241921.6%
 
231801.5%
 
271771.4%
 
Other values (286)467437.9%
 
ValueCountFrequency (%) 
0380.3%
 
16225.0%
 
24653.8%
 
34583.7%
 
44043.3%
 
53823.1%
 
63963.2%
 
73913.2%
 
83703.0%
 
93172.6%
 
ValueCountFrequency (%) 
7051< 0.1%
 
6861< 0.1%
 
5841< 0.1%
 
5341< 0.1%
 
5181< 0.1%
 
5171< 0.1%
 
5011< 0.1%
 
4861< 0.1%
 
4701< 0.1%
 
4491< 0.1%
 

ProductRelated_Duration
Real number (ℝ≥0)

ZEROS

Distinct9551
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1194.74622
Minimum0
Maximum63973.52223
Zeros755
Zeros (%)6.1%
Memory size96.5 KiB
2020-09-04T21:12:14.266665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1184.1375
median598.9369047
Q31464.157213
95-th percentile4300.289077
Maximum63973.52223
Range63973.52223
Interquartile range (IQR)1280.019713

Descriptive statistics

Standard deviation1913.669288
Coefficient of variation (CV)1.601737052
Kurtosis137.1741637
Mean1194.74622
Median Absolute Deviation (MAD)500.9369047
Skewness7.263227683
Sum14731220.89
Variance3662130.143
MonotocityNot monotonic
2020-09-04T21:12:14.396794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07556.1%
 
17210.2%
 
8170.1%
 
11170.1%
 
15160.1%
 
19150.1%
 
22150.1%
 
12150.1%
 
7140.1%
 
13140.1%
 
5130.1%
 
24130.1%
 
14130.1%
 
59120.1%
 
34120.1%
 
25120.1%
 
36110.1%
 
50110.1%
 
20100.1%
 
64100.1%
 
4100.1%
 
26100.1%
 
96100.1%
 
108100.1%
 
44100.1%
 
Other values (9526)1126491.4%
 
ValueCountFrequency (%) 
07556.1%
 
0.51< 0.1%
 
12< 0.1%
 
2.3333333331< 0.1%
 
2.6666666671< 0.1%
 
35< 0.1%
 
4100.1%
 
5130.1%
 
5.3333333331< 0.1%
 
65< 0.1%
 
ValueCountFrequency (%) 
63973.522231< 0.1%
 
43171.233381< 0.1%
 
29970.465971< 0.1%
 
27009.859431< 0.1%
 
24844.15621< 0.1%
 
23888.811< 0.1%
 
23342.082051< 0.1%
 
23050.104141< 0.1%
 
21857.046481< 0.1%
 
21672.244251< 0.1%
 

BounceRates
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1872
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219138047
Minimum0
Maximum0.2
Zeros5518
Zeros (%)44.8%
Memory size96.5 KiB
2020-09-04T21:12:14.537780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0031124675
Q30.0168125585
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.0168125585

Descriptive statistics

Standard deviation0.04848832181
Coefficient of variation (CV)2.185007006
Kurtosis7.723159431
Mean0.02219138047
Median Absolute Deviation (MAD)0.0031124675
Skewness2.947855267
Sum273.6197212
Variance0.002351117352
MonotocityNot monotonic
2020-09-04T21:12:14.677645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0551844.8%
 
0.27005.7%
 
0.0666666671341.1%
 
0.0285714291150.9%
 
0.051130.9%
 
0.0333333331010.8%
 
0.0251000.8%
 
0.016666667990.8%
 
0.1980.8%
 
0.04960.8%
 
0.02910.7%
 
0.022222222880.7%
 
0.0125850.7%
 
0.018181818810.7%
 
0.015384615760.6%
 
0.014285714730.6%
 
0.008333333720.6%
 
0.011111111620.5%
 
0.01610.5%
 
0.013333333540.4%
 
0.007692308530.4%
 
0.006666667510.4%
 
0.00952381510.4%
 
0.009090909470.4%
 
0.010526316450.4%
 
Other values (1847)426634.6%
 
ValueCountFrequency (%) 
0551844.8%
 
2.73e-051< 0.1%
 
3.35e-051< 0.1%
 
3.83e-051< 0.1%
 
3.94e-051< 0.1%
 
7.09e-051< 0.1%
 
7.27e-051< 0.1%
 
7.5e-051< 0.1%
 
8.01e-051< 0.1%
 
8.08e-051< 0.1%
 
ValueCountFrequency (%) 
0.27005.7%
 
0.1833333331< 0.1%
 
0.185< 0.1%
 
0.1769230771< 0.1%
 
0.1751< 0.1%
 
0.1666666674< 0.1%
 
0.1642857141< 0.1%
 
0.1642307691< 0.1%
 
0.1619047621< 0.1%
 
0.163< 0.1%
 

ExitRates
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4777
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04307279777
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Memory size96.5 KiB
2020-09-04T21:12:14.828646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004567568
Q10.014285714
median0.0251564025
Q30.05
95-th percentile0.2
Maximum0.2
Range0.2
Interquartile range (IQR)0.035714286

Descriptive statistics

Standard deviation0.04859654055
Coefficient of variation (CV)1.128242024
Kurtosis4.017034553
Mean0.04307279777
Median Absolute Deviation (MAD)0.0141725795
Skewness2.148789
Sum531.0875965
Variance0.002361623754
MonotocityNot monotonic
2020-09-04T21:12:14.968661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.27105.8%
 
0.13382.7%
 
0.053292.7%
 
0.0333333332912.4%
 
0.0666666672672.2%
 
0.0252241.8%
 
0.042141.7%
 
0.0166666671811.5%
 
0.021671.4%
 
0.0222222221521.2%
 
0.0285714291501.2%
 
0.0142857141181.0%
 
0.01251120.9%
 
0.0111111111030.8%
 
0.013333333870.7%
 
0.018181818870.7%
 
0.015384615830.7%
 
0.008333333780.6%
 
0760.6%
 
0.01700.6%
 
0.133333333670.5%
 
0.15600.5%
 
0.044444444580.5%
 
0.00952381540.4%
 
0.057142857540.4%
 
Other values (4752)820066.5%
 
ValueCountFrequency (%) 
0760.6%
 
0.0001755931< 0.1%
 
0.0002504381< 0.1%
 
0.0002621231< 0.1%
 
0.0002631581< 0.1%
 
0.0002923981< 0.1%
 
0.0004098361< 0.1%
 
0.0004464291< 0.1%
 
0.0004683841< 0.1%
 
0.0004807691< 0.1%
 
ValueCountFrequency (%) 
0.27105.8%
 
0.1923076921< 0.1%
 
0.1888888892< 0.1%
 
0.1866666674< 0.1%
 
0.1833333332< 0.1%
 
0.1818181821< 0.1%
 
0.180341881< 0.1%
 
0.183< 0.1%
 
0.1777777785< 0.1%
 
0.1756< 0.1%
 

PageValues
Real number (ℝ≥0)

ZEROS

Distinct2704
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.889257863
Minimum0
Maximum361.7637419
Zeros9600
Zeros (%)77.9%
Memory size96.5 KiB
2020-09-04T21:12:15.116663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.16052828
Maximum361.7637419
Range361.7637419
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.56843661
Coefficient of variation (CV)3.152933195
Kurtosis65.63569361
Mean5.889257863
Median Absolute Deviation (MAD)0
Skewness6.382964249
Sum72614.54945
Variance344.7868381
MonotocityNot monotonic
2020-09-04T21:12:15.249655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0960077.9%
 
53.9886< 0.1%
 
42.293067523< 0.1%
 
40.278152442< 0.1%
 
12.558857142< 0.1%
 
44.893459372< 0.1%
 
58.92417662< 0.1%
 
16.15855822< 0.1%
 
10.999018442< 0.1%
 
21.21126552< 0.1%
 
26.54552< 0.1%
 
59.9882< 0.1%
 
9.08476782< 0.1%
 
87.90296062< 0.1%
 
22.7382< 0.1%
 
34.039975362< 0.1%
 
40.40144812< 0.1%
 
54.982< 0.1%
 
14.12736982< 0.1%
 
78.569598642< 0.1%
 
42.4225312< 0.1%
 
15.39562< 0.1%
 
6.2210454552< 0.1%
 
2.2170294021< 0.1%
 
77.45798551< 0.1%
 
Other values (2679)267921.7%
 
ValueCountFrequency (%) 
0960077.9%
 
0.0380345421< 0.1%
 
0.0670495461< 0.1%
 
0.0935469491< 0.1%
 
0.0986214031< 0.1%
 
0.1206999141< 0.1%
 
0.1296768931< 0.1%
 
0.1318370131< 0.1%
 
0.1392006231< 0.1%
 
0.1506504981< 0.1%
 
ValueCountFrequency (%) 
361.76374191< 0.1%
 
360.95338391< 0.1%
 
287.95379281< 0.1%
 
270.78469311< 0.1%
 
261.49128571< 0.1%
 
258.54987321< 0.1%
 
255.56915791< 0.1%
 
254.60715791< 0.1%
 
246.75859021< 0.1%
 
239.981< 0.1%
 

SpecialDay
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06142741281
Minimum0
Maximum1
Zeros11079
Zeros (%)89.9%
Memory size96.5 KiB
2020-09-04T21:12:15.366662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1989172732
Coefficient of variation (CV)3.238249245
Kurtosis9.91365887
Mean0.06142741281
Median Absolute Deviation (MAD)0
Skewness3.302666747
Sum757.4
Variance0.03956808156
MonotocityNot monotonic
2020-09-04T21:12:15.456667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
01107989.9%
 
0.63512.8%
 
0.83252.6%
 
0.42432.0%
 
0.21781.4%
 
11541.2%
 
ValueCountFrequency (%) 
01107989.9%
 
0.21781.4%
 
0.42432.0%
 
0.63512.8%
 
0.83252.6%
 
11541.2%
 
ValueCountFrequency (%) 
11541.2%
 
0.83252.6%
 
0.63512.8%
 
0.42432.0%
 
0.21781.4%
 
01107989.9%
 

Month
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
May
3364 
Nov
2998 
Mar
1907 
Dec
1727 
Oct
549 
Other values (5)
1785 
ValueCountFrequency (%) 
May336427.3%
 
Nov299824.3%
 
Mar190715.5%
 
Dec172714.0%
 
Oct5494.5%
 
Sep4483.6%
 
Aug4333.5%
 
Jul4323.5%
 
June2882.3%
 
Feb1841.5%
 
2020-09-04T21:12:15.563690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-04T21:12:15.643705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:15.767970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.023357664
Min length3

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M527114.1%
 
a527114.1%
 
y33649.0%
 
N29988.0%
 
o29988.0%
 
v29988.0%
 
e26477.1%
 
c22766.1%
 
r19075.1%
 
D17274.6%
 
u11533.1%
 
J7201.9%
 
O5491.5%
 
t5491.5%
 
S4481.2%
 
p4481.2%
 
A4331.2%
 
g4331.2%
 
l4321.2%
 
n2880.8%
 
F1840.5%
 
b1840.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2494866.9%
 
Uppercase Letter1233033.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M527142.7%
 
N299824.3%
 
D172714.0%
 
J7205.8%
 
O5494.5%
 
S4483.6%
 
A4333.5%
 
F1841.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a527121.1%
 
y336413.5%
 
o299812.0%
 
v299812.0%
 
e264710.6%
 
c22769.1%
 
r19077.6%
 
u11534.6%
 
t5492.2%
 
p4481.8%
 
g4331.7%
 
l4321.7%
 
n2881.2%
 
b1840.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin37278100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M527114.1%
 
a527114.1%
 
y33649.0%
 
N29988.0%
 
o29988.0%
 
v29988.0%
 
e26477.1%
 
c22766.1%
 
r19075.1%
 
D17274.6%
 
u11533.1%
 
J7201.9%
 
O5491.5%
 
t5491.5%
 
S4481.2%
 
p4481.2%
 
A4331.2%
 
g4331.2%
 
l4321.2%
 
n2880.8%
 
F1840.5%
 
b1840.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37278100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M527114.1%
 
a527114.1%
 
y33649.0%
 
N29988.0%
 
o29988.0%
 
v29988.0%
 
e26477.1%
 
c22766.1%
 
r19075.1%
 
D17274.6%
 
u11533.1%
 
J7201.9%
 
O5491.5%
 
t5491.5%
 
S4481.2%
 
p4481.2%
 
A4331.2%
 
g4331.2%
 
l4321.2%
 
n2880.8%
 
F1840.5%
 
b1840.5%
 

OperatingSystems
Real number (ℝ≥0)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.124006488
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2020-09-04T21:12:15.856950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9113248287
Coefficient of variation (CV)0.4290593432
Kurtosis10.45684261
Mean2.124006488
Median Absolute Deviation (MAD)0
Skewness2.066285042
Sum26189
Variance0.8305129434
MonotocityNot monotonic
2020-09-04T21:12:15.947897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
2660153.5%
 
1258521.0%
 
3255520.7%
 
44783.9%
 
8790.6%
 
6190.2%
 
770.1%
 
56< 0.1%
 
ValueCountFrequency (%) 
1258521.0%
 
2660153.5%
 
3255520.7%
 
44783.9%
 
56< 0.1%
 
6190.2%
 
770.1%
 
8790.6%
 
ValueCountFrequency (%) 
8790.6%
 
770.1%
 
6190.2%
 
56< 0.1%
 
44783.9%
 
3255520.7%
 
2660153.5%
 
1258521.0%
 

Browser
Real number (ℝ≥0)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.357096513
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2020-09-04T21:12:16.039846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.717276676
Coefficient of variation (CV)0.7285559443
Kurtosis12.74673269
Mean2.357096513
Median Absolute Deviation (MAD)0
Skewness3.242349611
Sum29063
Variance2.94903918
MonotocityNot monotonic
2020-09-04T21:12:16.139784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
2796164.6%
 
1246220.0%
 
47366.0%
 
54673.8%
 
61741.4%
 
101631.3%
 
81351.1%
 
31050.9%
 
13610.5%
 
7490.4%
 
12100.1%
 
116< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
1246220.0%
 
2796164.6%
 
31050.9%
 
47366.0%
 
54673.8%
 
61741.4%
 
7490.4%
 
81351.1%
 
91< 0.1%
 
101631.3%
 
ValueCountFrequency (%) 
13610.5%
 
12100.1%
 
116< 0.1%
 
101631.3%
 
91< 0.1%
 
81351.1%
 
7490.4%
 
61741.4%
 
54673.8%
 
47366.0%
 

Region
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.147364152
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2020-09-04T21:12:16.235873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.401591237
Coefficient of variation (CV)0.7630484178
Kurtosis-0.1486803001
Mean3.147364152
Median Absolute Deviation (MAD)2
Skewness0.9835491595
Sum38807
Variance5.767640468
MonotocityNot monotonic
2020-09-04T21:12:16.327930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
1478038.8%
 
3240319.5%
 
411829.6%
 
211369.2%
 
68056.5%
 
77616.2%
 
95114.1%
 
84343.5%
 
53182.6%
 
ValueCountFrequency (%) 
1478038.8%
 
211369.2%
 
3240319.5%
 
411829.6%
 
53182.6%
 
68056.5%
 
77616.2%
 
84343.5%
 
95114.1%
 
ValueCountFrequency (%) 
95114.1%
 
84343.5%
 
77616.2%
 
68056.5%
 
53182.6%
 
411829.6%
 
3240319.5%
 
211369.2%
 
1478038.8%
 

TrafficType
Real number (ℝ≥0)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.069586375
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size96.5 KiB
2020-09-04T21:12:16.433866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.02516916
Coefficient of variation (CV)0.9890855703
Kurtosis3.479710597
Mean4.069586375
Median Absolute Deviation (MAD)1
Skewness1.962986732
Sum50178
Variance16.20198677
MonotocityNot monotonic
2020-09-04T21:12:16.536891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
2391331.7%
 
1245119.9%
 
3205216.6%
 
410698.7%
 
137386.0%
 
104503.6%
 
64443.6%
 
83432.8%
 
52602.1%
 
112472.0%
 
201981.6%
 
9420.3%
 
7400.3%
 
15380.3%
 
19170.1%
 
14130.1%
 
18100.1%
 
163< 0.1%
 
121< 0.1%
 
171< 0.1%
 
ValueCountFrequency (%) 
1245119.9%
 
2391331.7%
 
3205216.6%
 
410698.7%
 
52602.1%
 
64443.6%
 
7400.3%
 
83432.8%
 
9420.3%
 
104503.6%
 
ValueCountFrequency (%) 
201981.6%
 
19170.1%
 
18100.1%
 
171< 0.1%
 
163< 0.1%
 
15380.3%
 
14130.1%
 
137386.0%
 
121< 0.1%
 
112472.0%
 

VisitorType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.5 KiB
Returning_Visitor
10551 
New_Visitor
1694 
Other
 
85
ValueCountFrequency (%) 
Returning_Visitor1055185.6%
 
New_Visitor169413.7%
 
Other850.7%
 
2020-09-04T21:12:16.655966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-04T21:12:16.725940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:16.805895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length16.09294404
Min length5

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i3504117.7%
 
t2288111.5%
 
r2288111.5%
 
n2110210.6%
 
e123306.2%
 
_122456.2%
 
V122456.2%
 
s122456.2%
 
o122456.2%
 
R105515.3%
 
u105515.3%
 
g105515.3%
 
N16940.9%
 
w16940.9%
 
O85< 0.1%
 
h85< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter16160681.4%
 
Uppercase Letter2457512.4%
 
Connector Punctuation122456.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V1224549.8%
 
R1055142.9%
 
N16946.9%
 
O850.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i3504121.7%
 
t2288114.2%
 
r2288114.2%
 
n2110213.1%
 
e123307.6%
 
s122457.6%
 
o122457.6%
 
u105516.5%
 
g105516.5%
 
w16941.0%
 
h850.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_12245100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin18618193.8%
 
Common122456.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i3504118.8%
 
t2288112.3%
 
r2288112.3%
 
n2110211.3%
 
e123306.6%
 
V122456.6%
 
s122456.6%
 
o122456.6%
 
R105515.7%
 
u105515.7%
 
g105515.7%
 
N16940.9%
 
w16940.9%
 
O85< 0.1%
 
h85< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_12245100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII198426100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i3504117.7%
 
t2288111.5%
 
r2288111.5%
 
n2110210.6%
 
e123306.2%
 
_122456.2%
 
V122456.2%
 
s122456.2%
 
o122456.2%
 
R105515.3%
 
u105515.3%
 
g105515.3%
 
N16940.9%
 
w16940.9%
 
O85< 0.1%
 
h85< 0.1%
 

Weekend
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
9462 
True
2868 
ValueCountFrequency (%) 
False946276.7%
 
True286823.3%
 
2020-09-04T21:12:16.872080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Revenue
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
False
10422 
True
1908 
ValueCountFrequency (%) 
False1042284.5%
 
True190815.5%
 
2020-09-04T21:12:16.913063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-09-04T21:11:46.537356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:46.677339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:46.791273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:46.898212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.008149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.121097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.237003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.354946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.470879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.581742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.689730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.804742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:47.912731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.024744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.136744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.252738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.375735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.491744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.608741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.730734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.855742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:48.983734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.113189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.239116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.361046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.489973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.613901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.746826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:49.887745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.006687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.126619image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.235556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.347492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.467423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.586353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.704285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.819220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:50.928157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.035100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.163731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.292734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.416691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.541717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.660718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.787129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:51.895063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:52.006003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:52.120920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:52.237741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:52.356728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.376688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.522646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.641730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.756723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.867720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:53.985669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.100602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.219674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.345723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.461717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.586723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.712716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.834719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:54.963742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.092710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.219716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.335717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.456717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.569715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.698724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.831703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:55.945715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.070700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.186699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.306705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.429711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.550710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.677712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.803114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:56.925044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.039977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.166905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.290668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.416710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.540708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.666938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.826663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:57.953756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.080704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.208688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.337055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.471007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.603931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.733871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.861702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:58.992700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.114704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.243751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.378676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.503726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.633749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.760704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:11:59.892697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.024701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.156686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.291672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.424699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.555698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.705661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.846085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:00.973012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.116931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.265707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.383715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.509693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.646572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.767502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:01.917292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.078423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.212345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.342272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.466186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.595111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.719053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.832791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:02.956106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.079230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.196206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.317147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.425085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.537020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.653707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.771639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:03.890570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.010501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.125435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.237370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.353304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.460242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.574165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.691108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.808043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:04.932002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:05.048492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:05.171423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:05.306344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:05.432271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.064576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.196499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.317432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.436362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.556279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.669228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.788158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:06.913074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.023024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.136981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.243903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.356986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.477533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.594466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.717396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.839312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:07.958244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.068181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.184115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.324033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.441966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.557146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.677078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.803005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:08.918962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.037992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.165978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.294474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.424399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.552326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.681252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.805181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:09.929109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.044045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.166972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.295823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.417482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.543410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.663330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.801250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:10.941169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.078090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.220008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.358179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.492101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.622027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.755958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:11.883941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:12.018920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-04T21:12:16.995015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-04T21:12:17.239081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-04T21:12:17.480947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-04T21:12:17.730228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-04T21:12:18.497854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-04T21:12:12.331515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-04T21:12:12.671306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.00.0Feb1111Returning_VisitorFalseFalse
100.000.0264.0000000.0000000.1000000.00.0Feb2212Returning_VisitorFalseFalse
200.000.010.0000000.2000000.2000000.00.0Feb4193Returning_VisitorFalseFalse
300.000.022.6666670.0500000.1400000.00.0Feb3224Returning_VisitorFalseFalse
400.000.010627.5000000.0200000.0500000.00.0Feb3314Returning_VisitorTrueFalse
500.000.019154.2166670.0157890.0245610.00.0Feb2213Returning_VisitorFalseFalse
600.000.010.0000000.2000000.2000000.00.4Feb2433Returning_VisitorFalseFalse
710.000.000.0000000.2000000.2000000.00.0Feb1215Returning_VisitorTrueFalse
800.000.0237.0000000.0000000.1000000.00.8Feb2223Returning_VisitorFalseFalse
900.000.03738.0000000.0000000.0222220.00.4Feb2412Returning_VisitorFalseFalse

Last rows

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
1232000.0000.08143.5833330.0142860.0500000.0000000.0Nov2231Returning_VisitorFalseFalse
1232100.0000.060.0000000.2000000.2000000.0000000.0Nov1841Returning_VisitorFalseFalse
12322676.2500.0221075.2500000.0000000.0041670.0000000.0Dec2242Returning_VisitorFalseFalse
12323264.7500.0441157.9761900.0000000.0139530.0000000.0Nov22110Returning_VisitorFalseFalse
1232400.0010.016503.0000000.0000000.0376470.0000000.0Nov2211Returning_VisitorFalseFalse
123253145.0000.0531783.7916670.0071430.02903112.2417170.0Dec4611Returning_VisitorTrueFalse
1232600.0000.05465.7500000.0000000.0213330.0000000.0Nov3218Returning_VisitorTrueFalse
1232700.0000.06184.2500000.0833330.0866670.0000000.0Nov32113Returning_VisitorTrueFalse
12328475.0000.015346.0000000.0000000.0210530.0000000.0Nov22311Returning_VisitorFalseFalse
1232900.0000.0321.2500000.0000000.0666670.0000000.0Nov3212New_VisitorTrueFalse

Duplicate rows

Most frequent

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenuecount
2600.000.010.00.20.20.00.0Mar2211Returning_VisitorFalseFalse14
3600.000.010.00.20.20.00.0Mar3231Returning_VisitorFalseFalse7
4400.000.010.00.20.20.00.0May2213Returning_VisitorFalseFalse7
3800.000.010.00.20.20.00.0May1113Returning_VisitorFalseFalse6
1300.000.010.00.20.20.00.0Dec813920OtherFalseFalse5
3400.000.010.00.20.20.00.0Mar3211Returning_VisitorFalseFalse4
4100.000.010.00.20.20.00.0May1143Returning_VisitorFalseFalse4
6000.000.010.00.20.20.00.0Nov2211Returning_VisitorFalseFalse4
000.000.010.00.20.20.00.0Dec1111Returning_VisitorTrueFalse3
300.000.010.00.20.20.00.0Dec1141Returning_VisitorTrueFalse3